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Vetted LSTM Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in NLP, GenAI, and fraud/risk analytics
Mid-level Machine Learning Engineer specializing in NLP and AWS data pipelines
Mid-level AI/ML Engineer specializing in LLM, RAG, and agentic systems
Mid-level Machine Learning Engineer specializing in Generative AI, LLMs, and MLOps
Mid-level AI/ML Engineer specializing in LLMs, NLP, and MLOps
Mid-level Machine Learning Engineer specializing in MLOps, fraud detection, and data security
Mid-level Machine Learning & AI Engineer specializing in LLMOps, digital twins, and RL
Mid-level AI/ML Engineer specializing in Generative AI agents and workflow automation
Intern Software Engineer specializing in AI and full-stack web development
Junior Software Engineer specializing in full-stack development and applied ML
Junior AI Engineer specializing in distributed ML pipelines and time-series forecasting
Junior Machine Learning Software Engineer specializing in cloud-deployed predictive models
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Mid-level AI Software Engineer specializing in healthcare and agentic systems
Dhananjay Dubey
Senior Full-Stack Game Engineer specializing in multiplayer Unity and mobile systems
“Unity/C# game developer with hands-on experience shipping large-scale multiplayer mobile games, including titles cited at 1M+ and 10M+ downloads. Combines real-time networking and physics optimization expertise with AI/MR research experience, including an IEEE-published sports coaching system using pose estimation, SMPL-X, and LSTM models. Particularly strong in latency-sensitive, cross-platform interactive systems spanning mobile, multiplayer, and mixed reality.”
sai anuragh Sangoju
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP
“Built and deployed a production LLM-powered university support chatbot on Azure using a RAG pipeline, focusing on reducing hallucinations, improving latency, and handling ambiguous queries via confidence checks and clarification prompts. Also has hands-on orchestration experience (Airflow/Azure Data Factory), including hardening a demand-forecasting ingestion workflow with sensors, retries, and automated alerts, and uses a metrics-driven testing/monitoring approach for reliable AI agents.”
Yashwanth Penugonda
Mid-level AI/ML Engineer specializing in LLMs, RAG, and production GenAI systems
“Built and deployed a production LLM-powered RAG knowledge system to unify operational/policy information across PDFs, wikis, and databases, emphasizing auditability and low-latency/cost performance. Improved answer relevance at scale by moving from pure vector search to hybrid retrieval with metadata filtering and reranking, and partnered closely with healthcare operations/compliance to define acceptance criteria and human-in-the-loop guardrails.”
Shamala Chandrappa
Mid-level AI Engineer specializing in agentic AI, LLM systems, and healthcare AI
“Healthcare-focused ML/AI engineer who has built production voice agents and clinical question-answering systems end-to-end, from experimentation through deployment, observability, and iteration. Particularly strong in making LLM systems reliable in real workflows via RAG, fine-tuning, guardrails, evaluation pipelines, and shared Python tooling; cites ~20% clinical QA accuracy gains and ~40% faster physician decision turnaround.”
Vamsidhar Vuddagiri
Mid-level AI/ML Engineer specializing in LLMs, MLOps, and Azure
“AI/ML engineer who led Impacter AI’s production deployment of a specialized outreach LLM (CharmedLLM) fine-tuned on GPT-4.1, cutting API costs ~40% while boosting outreach effectiveness ~60%. Built the supporting MLOps and data infrastructure (MLflow, Kubernetes, PySpark, Kafka) and has agentic AI experience from University of Dayton, using LangChain + RAG and vector search (Pinecone) to improve reliability and reduce hallucinations.”